nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒08‒25
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Outlier Detection in Structural Time Series Models: the Indicator Saturation Approach By Martyna Marczak; Tommaso Proietti
  2. Dirichlet Process Hidden Markov Multiple Change-point Model By Ko, Stanley I. M.; Chong, Terence T. L.; Ghosh, Pulak
  3. Exponential Smoothing, Long Memory and Volatility Prediction By Tommaso Proietti
  4. Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts By Trojan, Sebastian

  1. By: Martyna Marczak (University of Hohenheim); Tommaso Proietti (Università di Roma “Tor Vergata” and CREATES)
    Abstract: Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse–and step–indicator saturation, we investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries.
    Keywords: Indicator saturation, seasonal adjustment, structural time series model, outliers, structural change, general–to–specific approach, state space model
    JEL: C22 C51 C53
    Date: 2014–11–08
  2. By: Ko, Stanley I. M.; Chong, Terence T. L.; Ghosh, Pulak
    Abstract: This paper proposes a new Bayesian multiple change-point model which is based on the hidden Markov approach. The Dirichlet process hidden Markov model does not require the specification of the number of change-points a priori. Hence our model is robust to model specification in contrast to the fully parametric Bayesian model. We propose a general Markov chain Monte Carlo algorithm which only needs to sample the states around change-points. Simulations for a normal mean-shift model with known and unknown variance demonstrate advantages of our approach. Two applications, namely the coal-mining disaster data and the real US GDP growth, are provided. We detect a single change-point for both the disaster data and US GDP growth. All the change-point locations and posterior inferences of the two applications are in line with existing methods.
    Keywords: Change-point; Dirichlet process; Hidden Markov model; Markov chain; Monte Carlo; Nonparametric Bayesian.
    JEL: C22
    Date: 2014–08–07
  3. By: Tommaso Proietti (DEF and CEIS, Università di Roma "Tor Vergata")
    Abstract: Extracting and forecasting the volatility of financial markets is an important empirical problem. Time series of realized volatility or other volatility proxies, such as squared returns, display long range dependence. Exponential smoothing (ES) is a very popular and successful forecasting and signal extraction scheme, but it can be suboptimal for long memory time series. This paper discusses possible long memory extensions of ES and finally implements a generalization based on a fractional equal root integrated moving average (FerIMA) model, proposed originally by Hosking in his seminal 1981 article on fractional differencing. We provide a decomposition of the process into the sum of fractional noise processes with decreasing orders of integration, encompassing simple and double exponential smoothing, and introduce a lowpass real time filter arising in the long memory case. Signal extraction and prediction depend on two parameters: the memory (fractional integration) parameter and a mean reversion parameter. They can be estimated by pseudo maximum likelihood in the frequency domain. We then address the prediction of volatility by a FerIMA model and carry out a recursive forecasting experiment, which proves that the proposed generalized exponential smoothing predictor improves significantly upon commonly used methods for forecasting realized volatility.
    Keywords: Realized Volatility. Signal Extraction. Permanent-Transitory Decomposition. Fractional equal-root IMA model
    JEL: C22 C53 G17
    Date: 2014–07–30
  4. By: Trojan, Sebastian
    Abstract: A high frequency stochastic volatility (SV) model is proposed. Price duration and associated absolute price change in event time are modeled contemporaneously to fully capture volatility on the tick level, combining the SV and stochastic conditional duration (SCD) model. Estimation is with IBM stock intraday data 2001/10 (decimalization completed), taking a minimum midprice threshold of a half tick. Persistent information flow is extracted, featuring a positively correlated innovation term and negative cross effects in the AR(1) persistence matrix. Additionally, regime switching in both duration and absolute price change is introduced to increase nonlinear capabilities of the model. Thereby, a separate price jump state is identified. Model selection and predictive tests show superiority of the regime switching extension in- and out-of-sample.
    Keywords: Stochastic volatility, stochastic conditional duration, non-Gaussian and nonlinear state space model, tick data, event time, generalized gamma distribution, negative binomial distribution, regime switching, Markov chain Monte Carlo, block sampler, particle filter, adaptive Metropolis
    JEL: C11 C15 C32 C58
    Date: 2014–08

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